




































Ecology, Economy and Society–the INSEE Journal 2 (2): 9–20, July 2019 

 
THEMATIC ESSAY  
 

Science, Uncertainty, and Society: Getting Beyond the 
Argument Culture to Shared Visions 
 

Robert Costanza  
 
Abstract: Practical problem-solving in complex societies requires the integration of 
three elements: (1) active and ongoing envisioning of both how the world works 
and how we would like the world to be, (2) systematic analysis appropriate to and 
consistent with the vision and (3) implementation appropriate to the vision. 
Scientists generally focus on the second step, but integrating all three is essential for 
both good science and effective, democratic decision-making. Subjective values 
enter the vision of broad social goals and the pre-analytic vision that necessarily 
precedes any form of scientific analysis. Because of this need for vision, completely 
objective scientific analysis is impossible. To better support democratic decision-
making, scholars of all varieties need to acknowledge the need to engage more 
directly in all three elements of the process while sharing their knowledge of how 
the world works and bringing their understanding of uncertainty more effectively to 
the table. This more integrated role of the scholars can help overcome the currently 
widespread denial of critical knowledge about how the world works, especially 
about climate, wellbeing, and evolution, and support better, more democratic 
decision-making about how we would like the world to be and how to get there. 

 

1. WHAT IS SCIENCE? 

The public and policy-makers often confuse science with ―objective‖ 
analysis. Because of the need for a vision, completely objective scientific 
analysis is impossible. Joseph Schumpeter put it this way: 

In practice we all start our own research from the work of our predecessors, that 
is, we hardly ever start from scratch. But suppose we did start from scratch, what 

                                                        
 VC‘s Chair in Public Policy, Crawford School of Public Policy, The Australian National 
University, 132 Lennox Crossing, Canberra, ACT 2601, Australia; 
robert.costanza@anu.edu.au 

Copyright © Costanza 2019. Released under Creative Commons Attribution-
NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v2i2.70  

https://doi.org/10.37773/ees.v2i2.70


Ecology, Economy and Society–the INSEE Journal [10] 

are the steps we should have to take? Obviously, in order to be able to posit to 
ourselves any problems at all, we should first have to visualize a distinct set of 
coherent phenomena as a worthwhile object of our analytic effort. In other words, 
analytic effort is of necessity is preceded by a preanalytic cognitive act that 
supplies the raw material for the analytic effort. In this book, this preanalytic 
cognitive act will be called Vision. It is interesting to note that vision of this kind 
not only must precede historically the emergence of analytic effort in any field, but 
also may reenter the history of every established science each time somebody 
teaches us to see things in a light of which the source is not to be found in the 
facts, methods, and results of the preexisting state of the science. (Schumpeter 
1954, 41) 

Nevertheless, it is possible to separate the process into a more subjective, or 
normative, envisioning component and a more systematic, less subjective 
analysis component (which is based on the vision). ―Good‖ science is that 
which makes clear its underlying preanalytic vision, and whose analysis is 
consistent with that vision. 

 

2. A CHANGING VISION OF SCIENCE 

The task would be simpler if the vision of science were static and 
unchanging. But as the quote from Schumpeter makes clear, this vision is 
itself evolving as we learn more. This does not invalidate science, as some 
deconstructionists would have it. Quite the contrary, by being explicit about 
their underlying preanalytic visions, scientists can enhance their honesty and 
thereby their credibility. Scientific credibility proceeds from an honest 
discussion of this underlying vision and its inherently subjective elements, 
as well as from constant, pragmatic testing of conclusions against real-world 
problems, rather than by appealing to non-existent objectivity. The 
preanalytic vision of science is changing from the ―logical positivist‖ view, 
which holds that science can discover ultimate truth by falsification of 
hypotheses, to the more pragmatic view that we do not have access to any 
ultimate, universal truths, but only to useful, abstract representations 
(models) of parts of the world. Science, in both the logical positivist and 
this ―pragmatic modelling‖ vision, works by building models and testing 
them. But the new vision recognizes that the tests are rarely, if ever, 
conclusive (especially in the life sciences and the social sciences); the 
models can only apply to a limited part of the real world; and the ultimate 
goal is therefore not the truth, but quality and utility. In the words of 
William Deming, ―All models are wrong, but some models are useful‖ 
(McCoy 1994). 

The primary goal of science, then, is the creation of models whose utility 
and quality can be tested against real world applications (Costanza 2001). 
The criteria by which one judges the utility and quality of models are 



[11] Robert Costanza 

themselves social constructs that evolve over time. There is, however, a 
fairly broad and consistent consensus in the scientific community about 
what these criteria are. They include (1) testability, (2) repeatability, (3) 
predictability, and (4) elegance (i.e., Occam‘s razor: The model should be as 
simple as possible, but no simpler!). But because of the nature of real-world 
problems, there are many applications for which some of these criteria are 
difficult or impossible to apply. These applications may nevertheless still be 
judged as ―good‖ science. For example, some purely theoretical models are 
not directly testable, but they may provide fertile ground for thought and 
debate and lead to more explicit models that are testable. Likewise, field 
studies of watersheds are not repeatable, strictly speaking, because no two 
watersheds are identical. But there is much we can learn from field studies 
that can be applied to other watersheds and tested against the other criteria 
of predictability and elegance. How simple a model can be depends on the 
nature, type and scope of the questions being asked. If we ask a more 
complex or more detailed question, the model will probably have to be 
more complex and detailed. As science progresses and the range of 
applications expands, the subjective criteria by which utility and quality are 
judged, must also adapt. This inherently subjective process goes on 
constantly within the scientific community. 

Scientists are not the only ones building models of the world. Every human, 
and indeed every sentient life form, employs models. A model, in this 
context, is any abstract, useful, representation of reality. We navigate the 
world using the model of the world each of us has in our brains. Our 
mental models or world-views guide our every action and determine how 
we interpret things that happen in the world and predict what will happen 
next (Gentner and Stevens 2014). Traditional and religious belief systems 
are also models of how the world works. Like all models, they are wrong, 
but they may be useful guides to behaviour while helping to build social 
capital. The discussion about traditional/religious models versus scientific 
models thus needs to move beyond ―truth‖ to the utility of the models in 
guiding behaviour and creating a sustainable and desirable future. From the 
point of view of utility, these models are not necessarily mutually exclusive 
(Wilson 2010). More on this further on.  

One often hears these days about ―cognitive bias‖ — the idea that we only 
perceive things that conform to our mental model of the way the world 
works (Caverni et al. 1990). Religious fundamentalists may reject data that 
does not conform to their mental model of a 6,000-year-old world. Climate-
deniers will not accept data of human-caused climate change if it does not 
conform to their mental model.  



Ecology, Economy and Society–the INSEE Journal [12] 

Science, as an enterprise, explicitly attempts to overcome confirmation bias 
by testing hypothesis against the real world and not existing mental models. 
For example, mental models based on the hypothesis of the inherent 
superiority of one subset of humans over others can be shown to be 
inconsistent with reality. But racist or misogynist mental models are difficult 
to change, in part because of confirmation bias. Science itself is not 
immune to confirmation bias, and there are many historical cases of 
theories that have ultimately been proven false hanging on in spite of 
overwhelming evidence to the contrary. But an important part of the issue 
is that, as argued above, there is often no definitive answer to complex 
problems. The best we can do is to develop useful models that acknowledge 
the uncertainty. How we deal with uncertainty is a key issue in the world of 
―fake news‖ and ―alternative facts.‖ 

 

3. DEALING WITH UNCERTAINTY 

One of the main issues with scientific uncertainty is not just its existence, 
but the radically different expectations and modes of operation that 
scientists, the public and policymakers have developed to deal with it. To 
solve this, these differences need to be understood and better methods 
designed to incorporate uncertainty into democratic decision making. 

To understand the scope of the problem, it is necessary to differentiate 
between risk and true uncertainty. Risk is an event with a known probability 
(sometimes referred to as statistical uncertainty). True uncertainty is an 
event with an unknown probability (sometimes referred to as 
indeterminacy) (Tversky and Fox 1995). For instance, every time you drive 
your car, you run the risk of having an accident because the probability of 
car accidents is well known. The risk involved in driving is well known 
because there have been many car accidents with which to calculate their 
historical frequency, which is taken as an estimate of the probability of 
having a car accident in the future. These probabilities are known with 
enough precision to be used by insurance companies, for instance, to set 
rates that will assure those companies of a certain profit. There is little 
uncertainty about the possibility of car accidents. If you live near the 
disposal site of some newly synthesized toxic chemical, however, your 
health may be in jeopardy, but no one knows to what extent. Because no 
one knows the probability of your getting cancer, for instance, or some 
other disease from this exposure. This is true uncertainty. Most important 
policy issues suffer from true uncertainty, not mere risk. 

Uncertainty may be thought of as a continuum ranging from zero for 
certain information to intermediate levels for information with statistical 



[13] Robert Costanza 

uncertainty and known probabilities (risk) to high levels for information 
with true uncertainty or indeterminacy. Risk assessment has become a 
central guiding principle in many government management agencies but 
true uncertainty is yet to be adequately incorporated. 

Scientists treat uncertainty as a given, a characteristic of all information that 
must be honestly acknowledged and communicated. Over the years, 
scientists have developed increasingly sophisticated methods to measure 
and communicate uncertainty arising from various causes. Scientists have 
often uncovered more uncertainty rather than the absolute precision that 
the lay public often mistakenly associates with scientific results. Scientific 
inquiry can only set boundaries on the limits of knowledge. It can define the 
edges of the envelope of known possibilities, but often the envelope is very 
large, and the probabilities of what‘s inside can be a complete mystery. For 
instance, scientists can describe the range of uncertainty about global 
warming and toxic chemicals and maybe say something about the relative 
probabilities of different outcomes, but, in most important cases, they 
cannot say which of the possible outcomes will occur with any degree of 
accuracy. Current approaches to management and policymaking, however, 
avoid uncertainty and gravitate to the edges of the scientific envelope. The 
reasons for this bias are clear. Policymakers want to make unambiguous, 
defensible decisions, which are often codified into laws and regulations. 
Although legislative language is often open to interpretation, regulations are 
much easier to write and enforce if they are stated in absolutely certain 
terms. For most of criminal law, the system works reasonably well. Either 
Cain killed his brother, or he did not; the only question is whether there is 
enough evidence to demonstrate guilt beyond a reasonable doubt (with 
essentially zero uncertainty). Because the burden of proof is on the 
prosecution, it does little good to conclude that there was an 80-percent 
chance that Cain killed his brother. But many scientific studies come to just 
these kinds of conclusions. Science defines the envelope while the policy 
process gravitates to an edge—usually the edge that best advances the 
policymaker‘s mental model and political agenda. But to use science 
rationally, democratic policy decisions must consider the whole envelope 
and all its contents. 

 

4. WHO BEARS THE BURDEN OF PROOF? 

A key question in dealing with uncertainty is: who bears the burden of 
proof? In many cases, in western democracies, the burden of proof has 
fallen on the public. This allows uncertainty to be manipulated to benefit 
private interests, who cannot be held responsible for damages until it is 



Ecology, Economy and Society–the INSEE Journal [14] 

proven that they were the cause. Given the discussion of uncertainty above, 
it should be clear that this is a severe burden of proof. It allows private 
interests to externalise the risks of their activities. This distorts market 
behaviour and leads to overuse and exploitation of the commons. A 
necessary condition for efficient markets is that all externalities be 
internalised. The question is how? If impacts are uncertain, how can we 
internalize them?  

Our current approach to dealing with the risk of private interests damaging 
public assets is to assign liability to the private interests with the burden of 
proof on the public. The public must demonstrate damages after the fact, 
claim compensation, endure a lengthy judicial process, and finally hope to 
recover just reparations. In addition, the total liability is often limited. For 
example, in the U.S., the Oil Pollution Act of 1990 limits the liability for oil 
spills to USD 75 million and the Price-Anderson Act limits the liability for 
nuclear power plant accidents to USD 10 billion. The Exxon Valdez oil spill 
resulted in an estimated USD 3.4 billion in fines, compensation, and clean-
up costs, and a court settlement of USD 2.5 billion in punitive damages that 
took decades of lawsuits after the incident and was ultimately reduced by 
the Supreme Court to USD 500 million in 2008 (Maag 2008). 

In many other parts of society, we require private interests to buy insurance 
to deal with the risks they impose on the public. For example, purchasing 
automobile insurance is often mandatory, and assurance bonds are often 
required from building contractors. Requiring assurance bonds or insurance 
forces private interests to internalize the risk of their activities before any 
damages occur. It gives them strong financial incentives to reduce risk, 
since it is their own money that they stand to lose. The Deepwater Horizon 
incident, like the banking crisis, resulted from inadequate attention to the 
risks that the public was left to bear. Precautionary measures were known 
but not taken. Investments in safety devices (like the acoustic blowout 
preventer) were not made. Corners were cut. Short-term private profits 
motivated taking high risks with public assets. The fundamental problem is 
that while private interests are ultimately liable for damages to public assets, 
they are only held accountable long after the fact and only partially. This 
gives private interests strong incentives to take large risks with public 
assets—far larger than they should from society‘s point of view. If society 
does not change investment incentives, private interests will continue to 
devote vast sums of capital to pursue increasingly risky oil reserves (or 
financial products) that provide less net energy and maintain our oil 
addiction—an addiction which simply cannot be physically sustained. It also 
encourages climate inaction, since costs and liabilities can be externalized 
and pushed into the future.  



[15] Robert Costanza 

One way to internalize these risks would be to require private interests to 
post an ―assurance bond‖ large enough to cover the worst-case damages 
(Costanza and Perrings 1990; Costanza and Cornwell 1992). Portions of or 
the entire bond (plus interest) would be returned, if and when the private 
interests demonstrate that the suspected worst-case damages had not 
occurred or would be less than was originally assessed. If damages did 
occur, portions of the bond would be used to rehabilitate or repair the 
assets and to compensate injured parties. The critical feature is that the risk 
to the public asset is apparent to the private interests in financial terms 
before the fact, not as a liability that may or may not be enforced after the 
damage occurs. Science can contribute to this process substantially, because 
it is often easier to quantify the worst-case scenario than to identify where 
within the range of uncertainty the impact may fall.  

Consider the impact of fossil fuel use on climate. Climate deniers argue that 
there is no impact while the scientific community presents a range of 
estimates that acknowledge the uncertainty, including a worst-case scenario. 
What if the fossil fuel producers were required to post an assurance bond to 
cover the potential worst-case impact of carbon emissions from fossil fuels, 
in addition to internalizing the social cost of carbon emissions with a 
carbon tax? The speed of transition to renewables that this would cause 
would be amazing. 

 

5. MANIPULATING UNCERTAINTY IN THE ARGUMENT 
CULTURE  

Uncertainty can be manipulated for political purposes. The climate 
―debate‖ is the most obvious current example. The scientific community, as 
summarised in the IPCC reports, clearly lays out what is known about the 
changing climate, its causes, and the degree of uncertainty in each element 
of the assessments. Climate deniers seize on the fact that there is 
uncertainty to argue that the assessments lack any credibility and cannot 
―prove‖ that humans cause climate change.  

This feeds into what Deborah Tannan (1998) has called the ―argument 
culture.‖ In this culture, even the most complex problems are cast as polar 
opposites with no uncertainty. All discussions are cast as a debate between 
two extremes in which one side is right while the other is wrong. The 
media, the law, politics, and academia are all caught in the argument culture, 
and its influence and control over our lives is increasing. The problem is 
that, while there is nothing inherently wrong with debate and direct 
confrontation on some topics, it does not work for all topics. Certainly, the 
complex problems that are the focus of democratic decision-making require 



Ecology, Economy and Society–the INSEE Journal [16] 

a more multifaceted, complex approach—one that encourages real dialogue 
and does not cast every discussion as a zero-sum, win-lose, either-or 
dichotomy. 

As Tannen notes: 

Throughout our educational system the most pervasive inheritance is the 
conviction that issues have two sides, that knowledge is best gained through 
debate, that ideas should be presented orally to an audience that does its best to 
poke holes and find weaknesses, and that to get recognition, one has to ‗stake out 
a position‘ in opposition to another. (Tannan 1998, 261) 

The argument culture pervades our political process and makes truly 
democratic decision-making difficult, if not impossible. The ‗winner take 
all‘, win-lose, two party system is locked in to the argument culture and 
cannot support informed discussion of issues that acknowledge the 
fundamental uncertainties involved. Democracy should be about building a 
broad consensus about shared goals and the policies to achieve them. 
Instead, the argument culture obscures the complexity of the world and 
allows disinformation and distortion to flourish.  

Tannen goes on to challenge us to find ways to go beyond the argument 
culture: ‗‗It will take creativity to find ways to blunt the most dangerous 
blades of the argument culture. It‘s a challenge we must undertake, because 
our public and private lives are at stake‘‘ (1998, 290) 

We need to further develop processes like deliberative democracy (Drysack 
2010) that can facilitate engaged discussion among a broad range of 
stakeholders about complex issues, rather than confrontational debates. 
Scientists are a key group in these processes, especially as they bring their 
understanding of uncertainty into the discussions. As Buchanan (1954) put 
it: ―The definition of democracy as ‗government by discussion‘ implies that 
individual values can and do change in the process of decision-making‖ 
(Buchanan 1954, 120). To reinvent democracy, we have to reinvent 
discussion and move beyond the argument culture. 

 

6. WHAT CAN WE DO? 

Science is critical to the process of building a shared-vision of the world we 
want and implementing policies to achieve that vision. But the system today 
is locked-in or addicted to patterns of behaviour that prevent the needed 
transformation. Societies, like individuals, can get trapped in patterns of 
behaviour called social traps or ―societal addictions‖ that provide short-
term rewards but are detrimental and unsustainable in the long-run 
(Costanza 1987). Examples include our societal addiction to inequitable 



[17] Robert Costanza 

over-consumption fuelled by fossil energy and a ―growth at all costs‖ 
economic model. We can learn from therapies that work at the individual 
level to help develop therapies that might work at the societal level 
(Costanza et al. 2017). In particular, Motivational Interviewing (MI) is one 
of the most effective therapies at the individual level. It is based on 
engaging addicts in a positive discussion of their goals, motives, and futures. 
One analogy to MI at the societal level is a modified version of scenario 
planning (SP) that has been extended to engage the entire community 
(community scenario planning, CSP) in thinking about goals and alternative 
futures via public opinion surveys and deliberative forums. Both MI and 
CSP are about exploring alternative futures in positive, non-confrontational 
ways and building commitment or consensus about preferred futures. 
Effective therapies for societal addictions may be possible, but, as we learn 
from MI, they will require a rebalancing of effort away from only pointing 
out the dire consequences of current behaviour (without denying those 
consequences) towards building a shared vision of a positive future, and the 
means to get there. 

In the policy sphere, science has mainly contributed to pointing out the dire 
consequences of the current behaviour. But part of the reason this science 
is now being ignored by some is that it does not conform to their mental 
model of how the world works. Climate deniers are indeed in denial about 
the science of global climate change, in the same way that drug addicts or 
cigarette smokers can be in denial of the well-known harmful effects of 
their habit.  

At the individual level, MI techniques engage with addicts in a non-
judgmental way to help them develop a positive vision of a better life for 
themselves that is based on their deepest values. Such a vision can often 
motivate a substantial change. This is what a strategy of scenario planning 
and envisioning extended to include public opinion surveys and broad 
societal dialogue about what alternative futures could provide at the societal 
level. What is necessary to implement this strategy is to fully engage the 
larger society in discussing and sharing alternative futures and building 
consensus on preferred futures. Putting future scenarios out to the public in 
the form of public opinion surveys (Costanza et al. 2015, Chambers et al. 
2019), dialogues, media events, films, videos, and other approaches can do 
this, but this is a largely unexplored area and is certainly a far cry from 
politics as usual. Workshops with a broad range of stakeholders from across 
the political spectrum have consistently shown that if the question is: ―what 
kind of world do you want in the future?‖ there is much broader consensus 
than one would imagine based on current, polarized positions. In order to 



Ecology, Economy and Society–the INSEE Journal [18] 

bring people together we need to first focus on developing a shared vision 
of the future, and this will not be as difficult as it at first appears. 

There is ample room for creative design and testing of a range of societal 
therapies to build this shared vision and escape the argument culture. 
Scaling up what works at the individual level may be an important path for 
more effective societal therapies that will allow us to build a truly 
democratic, sustainable, and desirable future together. 

 

7. SUMMARY AND CONCLUSIONS 

I sum up my observations as follows: 

(i) There is no such thing as scientific objectivity, because all science 
must be (1) based on a pre-analytic vision that is inherently subjective 
and (2) judged for utility and quality against criteria that are inherently 
subjective. We can, however, be very clear about the distinction 
between the vision and values component of the process and the 
analysis component built on that vision. 

(ii) The quality of scientific work can thus be judged based on its 
adherence to the pre-analytic vision and its pragmatic utility in 
modelling the real world, as tested against the general criteria developed 
by the scientific community. We can judge between ―good‖ science and 
―bad‖ science according to these subjectively determined criteria of 
quality, but it is not really honest or useful to use objectivity as a 
yardstick. 

(iii) Subjective values also enter the discussion when we talk about how 
we would like the world to be. This aspect of future visions strongly 
determines which set of current policies are most appropriate, given the 
huge level of uncertainty about the current and future state of the 
world. 

(iv) The major source of uncertainty about our current policies is at this 
level of visions and worldviews, not in the details of analysis or 
implementation within a particular vision.  

(v) Democracy should be about building a shared vision of the world we 
want and implementing policies to achieve that vision. The Sustainable 
Development Goals (SDGs) are a major step in the right direction. 

(vi) By developing alternative future scenarios, the critical assumptions 
and uncertainties underlying each vision can be more easily seen. The 
broader public can be engaged (via public opinion surveys, deliberative 



[19] Robert Costanza 

fora, and other methods) to overcome the argument culture and build a 
broad consensus on the future we want and how to get there. 

 

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